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Ministral 3 14B 2512

  • Text Generation

Ministral 3 14B 2512 is a 14-billion-parameter AI language model from Mistral’s Ministral 3 series, configured with a 2,512-dimensional internal representation. It is designed to provide a balance of capability and efficiency for general-purpose text understanding and generation.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is Ministral 3 14B 2512?

Ministral 3 14B 2512 is a medium-sized transformer-based language model developed by Mistral within the Ministral 3 line. It is mainly used for tasks such as conversation, drafting, summarization, and code or data-assisted text generation. It is also applied in applications that need relatively strong reasoning and language skills while remaining efficient enough for practical deployment. It belongs to Mistral’s Ministral 3 family of models, which extends the company’s earlier Mistral and Mixtral model series.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn, instruction-following conversations, answering questions and following user intent across diverse general-purpose topics.

  • Code Reasoning

    Understands and writes code snippets in common programming languages, explaining logic, fixing simple bugs, and suggesting improvements.

  • Multilingual Translation

    Translates text between major languages, preserving meaning and tone for instructions, explanations, and everyday content.

  • Document OCR

    Extracts and structures text from images or scanned documents, enabling downstream processing and analysis of the recognized content.

  • Image Understanding

    Interprets images by identifying entities and relationships, then producing natural-language descriptions and answering related visual questions.

Use cases

6 Most Valuable Use Cases

  • General Text Chatbot
  • Code Generation Helper
  • Document Summarization
  • Legal Text Review
  • Contract Change Monitoring
  • Product Description Drafting

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and fastest access to Ministral 3 14B–class models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global ~120ms ~200 tps 99.99% ~$0.08 ~$0.24 ~256K
Mistral EU West ~220ms ~120 tps 99.9% ~$0.15 ~$0.45 ~256K
OpenRouter Global ~260ms ~90 tps 99.9% ~$0.18 ~$0.54 ~128K
Together AI US East ~240ms ~130 tps 99.9% ~$0.14 ~$0.42 ~128K
Anyscale US West ~250ms ~100 tps 99.9% ~$0.16 ~$0.48 ~128K

Performance benchmarks

Technical Specifications

Metric Ministral 3 14B 2512 (Mistral) Llama 3.1 8B (Meta) GPT-4o mini (OpenAI)
Avg Latency ~180ms ~220ms ~230ms
Context Window 128K 8K 8K
Input Price ($/1M tokens) $0.20 $0.10 $0.12
Output Price ($/1M tokens) $0.60 $0.40 $0.45
Max Output Tokens 4K 4K 4K
Throughput 60 tps 45 tps 40 tps
Uptime 99.9% 99.5% 99.9%

30-day usage via LLM API

18.5B
Prompt tokens processed (last 30 days)
5.4B
Completion tokens generated (last 30 days)
11.2M
API requests served (last 30 days)
99.8%
Avg uptime over 30 days
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Automatically route each request to the optimal model across providers based on latency, cost, or quality—without changing your code or client integration.

    One endpoint, every model.

  • Cost-Aware Orchestration

    Dynamically balance premium and budget models, enforce spend limits, and visualize per-provider costs so you can ship faster without surprise bills.

    Optimize spend by default.

  • Automatic Fallback Safety

    When a provider fails, times out, or degrades, requests transparently fail over to healthy models so your production apps stay online and responsive.

    No single-provider outages.

  • Deep LLM Observability

    Track latency, errors, tokens, and success metrics across every provider and model with built-in traces and logs, ready for dashboards and alerts.

    See every token, everywhere.

  • Task-Level Orchestration

    Define high-level tasks instead of individual models; LLM.API picks the right provider, parameters, and tools for each job automatically.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Submit massive batches of prompts in a single request with provider-aware throttling, retries, and aggregation to keep pipelines fast and cost-efficient.

    Scale workloads, not code.

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a compact general-purpose model that balances capability, latency, and cost effectively.
  • You need to power chatbots or assistants where mid-tier reasoning is sufficient.
  • Your use case involves summarizing or transforming medium-length documents without extreme accuracy requirements.
  • Your use case involves prototyping AI features before committing to larger, pricier models.
  • You need a smaller model suitable for on-prem or edge deployment with constrained resources.
  • Your use case involves multilingual text tasks where good, but not expert-level, fluency suffices.

Avoid if...

  • You need frontier-level reasoning, planning, or coding performance comparable to the strongest flagship models.
  • Your workload requires highly reliable long-context understanding across very large documents or codebases.
  • You need state-of-the-art performance on complex math, scientific, or safety-critical tasks.
  • Your workload requires best-in-class coding assistance for large, interconnected repositories and refactors.
  • You need advanced tool use, multi-step agents, or orchestrated workflows demanding top reasoning accuracy.
  • Your workload requires heavily optimized inference on specialized hardware already tuned for different architectures.

FAQ

Frequently Asked Questions

  • What is Ministral 3 14B 2512?

    Ministral 3 14B 2512 is a 14B-parameter Mistral language model available through LLM.API for fast, cost-efficient text generation and reasoning.

  • What is Ministral 3 14B 2512 best suited for?

    It is best for general-purpose chat, code assistance, lightweight agents, and applications needing a strong balance of quality, speed, and price.

  • What context window does Ministral 3 14B 2512 support on LLM.API?

    Ministral 3 14B 2512 supports a 32K token context window for prompts plus responses on LLM.API.

  • How fast is Ministral 3 14B 2512 in terms of latency and throughput?

    Typical latency is low hundreds of milliseconds for short prompts, with high token-per-second throughput suitable for interactive applications.

  • What modalities does Ministral 3 14B 2512 support?

    Ministral 3 14B 2512 is a text-only model, supporting text input and text output only.

  • How is Ministral 3 14B 2512 priced on LLM.API?

    LLM.API charges per 1,000 tokens of input and output; check the LLM.API pricing page for current Ministral 3 14B 2512 rates.

  • How do I access Ministral 3 14B 2512 via LLM.API?

    Call the LLM.API chat or completions endpoint with the model parameter set to the Ministral 3 14B 2512 identifier and your API key.

  • How does Ministral 3 14B 2512 compare to similar models?

    Compared with larger frontier models, it offers lower latency and cost while delivering mid-to-high-tier quality for common coding and reasoning tasks.

  • What are the main limitations of Ministral 3 14B 2512?

    It can hallucinate, lacks real-time knowledge or tools, and may underperform very large models on complex multi-step reasoning or niche domains.

  • Can I use Ministral 3 14B 2512 for batch and streaming workloads?

    Yes, LLM.API supports both standard batched requests and optional token streaming for Ministral 3 14B 2512, depending on your integration.

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